Fuzzy Modeling and Fuzzy Control (Control Engineering) - Hardcover

Zhang, Huaguang; Liu, Derong

Hardcover

ISBN 10: 0817644911 ISBN 13: 9780817644918

Verlag: Birkhäuser, 2006

Inhaltsangabe

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

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Über die Autorin bzw. den Autor

Jiayue Sun received the Ph.D. degree in power electronics and power transmission from Northeastern University in Shenyang, China, under the supervision of IEEE Fellow Huaguang Zhang, in 2021. She is a postdoctoral fellow under Tianyou Chai (a member of the Chinese Academy of Engineering, IEEE Life Fellow), in the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. She is currently a Teacher with Northeastern University. She has authored or coauthored 40 peer-reviewed international journal papers. Her current research interests include optimization of complex industrial processes, intelligent adaptive learning, and distributed control of multiagent systems.

Dr. Sun is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Chinese Association of Automation (CAA) and Chinese Association for Artificial Intelligence (CAAI), where she works as the Intelligent Adaptive Cooperative Optimization Control Committee's Vice Secretary-General.

Shun Xu received the B.S. degree in clinical medicine, the M.S. degree in surgery, and the Ph.D. degree in thoracic surgery from China Medical University, Shenyang, China, in 1987, 1990, and 1995, respectively. He studied in Japan from 1994 to 1996. He is a chief physician, director of thoracic surgery, and director of the Lung Cancer Research Laboratory at China Medical University's Cancer Institute. He is also a national second-level professor and a doctoral supervisor. He has presided over and participated in a number of national, provincial, and ministerial level research works. His research interests include applications of reinforcement learning, convolution neural network, and pattern recognition in medical image, especially for diagnosis of lung cancer, esophageal cancer, and other thoracic tumors.

Dr. Xu has been awarded the State Council Special Allowance from the State Council. He is a member of the ThoracicCardiovascular Surgery Branch Committee of the Chinese Medical Association. He serves as the chairman of the Thoracic Surgery Branch Committee of the Liaoning Medical Association.

Yang Liu is a medical doctor specializing in thoracic surgery. He holds the positions of associate professor, associate chief physician, and master's supervisor for graduate students. He has authored numerous articles in both national and international medical journals, focusing on the topics of lung cancer and tumors. His research interests include reinforcement learning, adaptive dynamic programming, neural networks, and their applications in diagnosing lung diseases.

Dr. Liu is a member of the Cell Biology Cardiopulmonary Rehabilitation Committee in Liaoning Province. His primary research area revolves around standardized treatment for lung cancer and mediastinal tumors. He has led a youth fund of the National Natural Science Foundation of China, and has also participated in the National Natural Science Foundation of China (General Project). Additionally, he serves as an expert for the National Natural Science Foundation Committee.

Huaguang Zhang received the B.S. and M.S. degrees in control engineering from the Northeast Dianli University of China, Jilin City, China, in 1982 and 1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a Postdoctoral Fellow, for two years, where he has been a Professor and the Head of the Institute of Electric Automation, College of Information Science and Engineering since 1994. He has authored or coauthored over 200 journal and conference papers and four monographs and has co-invented 20 patents. His current research interests include fuzzy control, stochastic-system control, neural-network-based control, nonlinea

Von der hinteren Coverseite

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems.

Based on three types of fuzzy models―the Mamdani fuzzy model, the Takagi–Sugeno fuzzy model, and the fuzzy hyperbolic model―the book addresses a number of important issues in fuzzy control systems, including fuzzy modeling, fuzzy inference, stability analysis, systematic design frameworks, robustness, and optimality. The authors develop several advanced control schemes, such as the fuzzy model-based generalized predictive control scheme, the fuzzy adaptive control scheme based on fuzzy basis function vectors, the fuzzy control scheme based on fuzzy performance evaluators, and the fuzzy sliding-mode control scheme. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

Details zur Ausgabe

• Verlag: Birkhäuser
• Erscheinungsdatum: 2006
• Sprache: Englisch
• ISBN 10: 0817644911
• ISBN 13: 9780817644918
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 432
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland